1. Field
The following relates to computer vision, and more particularly, to a method and apparatus for tracking movable objects using a plurality of communicatively-coupled stereo sensors.
2. Related Art
Currently, the field of computer vision is undergoing an explosion in research and development with respect to being able detect and track moving objects using images captured by a plurality or “network” of image sensors. Being able to detect and track the movable objects from images captured by the network image sensors beneficially provides a platform or mechanism upon which many important applications, including visual surveillance, intelligent living environment, human behavior analysis, etc., can be conceived, designed, constructed, developed or otherwise built.
Being networked together, the network of image sensors may be configured to have a collective field of view (“CFOV”) that covers a wide area (that is, a field of view that covers a large spatial and/or a long duration temporal domain). As such, the images of the moveable objects captured by the network of image sensors provide the ability to track the movable objects across such wide area. The CFOV provided by the network of image sensors is potentially much more powerful than a field of view of a single image sensor (“SFOV”), which tends to be narrower than the CFOV or, if the same, then images captured by the single image sensor have resolutions much lower than images captured in the CFOV. Accordingly, the SFOV may detrimentally prevent the single image sensor from detecting and/or tracking objects as they undergo many interesting events across the wide area.
On the other hand, the CFOV allows the network of image sensors to provide images for continuous or, conversely, intermittent tracking of the movable objects, such as humans of vehicles, across the wide area. In turn, the tracking of the movable objects by the network of image sensors may provide the ability to determine and maintain identities of the movable objects throughout the wide area.
Tracking objects using “blobs” (“blob tracking”) is a popular low-cost approach for tracking the movable objects using a series of sequential images (“frames”). Blob tracking of a given one of the movable objects, at its most basic level, entails (i) extracting from each of the frames blobs that are theoretically representative of each of the movable objects, and (ii) associating the blobs in a first frame to a second frame, and so on. Blob tracking, however, is not a viable tracking mechanism because proximities between the blobs for the multiple movable objects and occlusions tend to merge the blobs into a single blob. As such, the blob tracking lacks the ability to distinguish one of the movable objects from another, and as such, becomes untrustworthy.
Thus, there is a need in the art for a system and method for tracking moveable objects over a wide area, where the tracking is capable and trustworthy of distinguishing one of the moveable objects from another.